system

Abstract

The system according to this embodiment aims to centrally manage personal data and promote the use of generated AI. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, and a data provision unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The data provision unit provides the analysis results obtained by the analysis unit.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, since personal data is dispersed across various platforms, it is difficult to manage the data centrally, and there is a problem that the utilization of generative AI is restricted.

[0005] The system according to the embodiment aims to centrally manage personal data and promote the utilization of generative AI.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The provision unit provides the analysis result obtained by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment can centrally manage personal data and promote the use of generating AI. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The personal data platform according to an embodiment of the present invention is a system that securely aggregates personal data and creates new value by utilizing generative AI. This personal data platform consists of three main elements: a data collection interface, a generative AI, and an analysis result provision interface. The data collection interface automatically collects data from various platforms and stores it securely. The generative AI analyzes the collected data and extracts information useful to the individual. The analysis result provision interface visually displays the analysis results so that individuals can easily understand them. This allows individuals to effectively utilize their own data and improve their quality of life. In terms of security, it is equipped with functions for data encryption and access control. For example, specific applications can be considered such as health management, financial management, and lifestyle optimization. Thus, the personal data platform can effectively utilize personal data and improve the quality of life.

[0029] The personal data platform according to this embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects data. The collection unit automatically collects data from various platforms, for example, and stores it securely. The collection unit collects data using APIs, for example. The collection unit can also collect data using scraping techniques. Furthermore, the collection unit has functions for data encryption and access control. For example, the collection unit encrypts data using encryption technologies such as AES or RSA. The collection unit also has access control functions such as authentication, authorization, and role-based access control. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using generative AI, for example. Generative AI includes text generation AI (e.g., LLM) and multimodal generation AI. The analysis unit extracts statistical information and trend information from the data, for example. The analysis unit can also apply different analysis algorithms depending on the data category. For example, the analysis unit applies a health management-specific analysis algorithm to health data. The provision unit provides the analysis results obtained by the analysis unit. The data provider, for example, visually displays the analysis results so that individuals can easily understand them. The data provider displays the analysis results in the form of graphs, charts, dashboards, etc. The data provider can also estimate the user's emotions and adjust the way the information is displayed based on the estimated emotions. For example, if the data provider is feeling stressed, it provides a simple and highly visible display method. As a result, the personal data platform according to this embodiment can effectively utilize personal data and improve the quality of life.

[0030] The data collection unit can automatically collect data from multiple platforms and store it securely. For example, the data collection unit can automatically collect data from multiple platforms using APIs. For instance, it can collect data from social media, websites, and applications. The data collection unit can also collect data using scraping techniques. For example, it can analyze the content of web pages and extract the necessary data. Furthermore, the data collection unit securely stores the collected data. For example, it can encrypt the data using encryption technologies such as AES or RSA. The data collection unit also includes access control functions such as authentication, authorization, and role-based access control. This enables centralized data management by automatically collecting and securely storing data. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input data acquired using APIs into a generating AI and have the generating AI perform data analysis.

[0031] The analysis unit can analyze the collected data and extract information useful to the individual. For example, the analysis unit can analyze the collected data using generative AI. Generative AI can be text generation AI (e.g., LLM) or multimodal generation AI. The analysis unit can extract statistical information and trend information from the data. For example, the analysis unit can analyze an individual's health status from the collected data and provide information useful for health management. The analysis unit can also analyze an individual's financial situation from the collected data and provide information useful for financial management. Furthermore, the analysis unit can analyze an individual's lifestyle from the collected data and provide information useful for optimizing their lifestyle. For example, the analysis unit can analyze an individual's diet and exercise data and suggest a healthy lifestyle. In this way, by analyzing the data and extracting useful information, individuals can effectively utilize the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generative AI and have the generative AI perform the data analysis.

[0032] The data provider can visually display the analysis results so that individuals can easily understand them. For example, the data provider can visually display the analysis results in the form of graphs, charts, dashboards, etc. For example, the data provider can display a graph showing an individual's health status and provide information useful for health management. The data provider can also display a chart showing an individual's financial situation and provide information useful for financial management. Furthermore, the data provider can display a dashboard showing an individual's lifestyle and provide information useful for optimizing their lifestyle. For example, the data provider can visually display an individual's diet and exercise data and suggest a healthy lifestyle. By visually displaying the analysis results, individuals can easily understand the content of the data. Some or all of the above processing in the data provider may be performed using AI, for example, or not using AI. For example, the data provider can input the analysis results into a generating AI and have the generating AI perform the visual display.

[0033] The data collection unit can be equipped with data encryption and access control functions. The data collection unit encrypts data using encryption technologies such as AES or RSA. For example, the data collection unit encrypts collected data using AES encryption technology and stores it securely. The data collection unit also has access control functions such as authentication, authorization, and role-based access control. For example, the data collection unit authenticates users accessing the data, and only authorized users can access the data. Furthermore, the data collection unit can use role-based access control to set access rights according to the user's role. This enhances data security through data encryption and access control. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have a generating AI perform data encryption and access control settings.

[0034] The analysis unit can provide specific application examples for health management, financial management, and lifestyle optimization. For example, the analysis unit can analyze collected data and provide information useful for health management. For example, the analysis unit can analyze an individual's health status and provide information useful for fitness tracking and diet management. The analysis unit can also analyze collected data and provide information useful for financial management. For example, the analysis unit can analyze an individual's financial situation and provide information useful for budget management and investment analysis. Furthermore, the analysis unit can analyze collected data and provide information useful for lifestyle optimization. For example, the analysis unit can provide information useful for an individual's time management and habit formation. By providing specific application examples, individuals can utilize data in a variety of fields. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input collected data into a generating AI and have the generating AI generate information useful for health management, financial management, and lifestyle optimization.

[0035] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit uses AI to analyze the user's past data collection history. For example, the data collection unit inputs past collection dates and types of collected data into the AI ​​and selects the optimal collection method. For example, the data collection unit prioritizes collecting data from platforms that the user has frequently used in the past. The data collection unit can also identify the most efficient collection time slots from the user's past data collection history. Furthermore, the data collection unit can optimize the data to be collected based on the types of data the user has collected in the past. For example, the data collection unit prioritizes collecting health-related data based on health data the user has collected in the past. This allows the optimal collection method to be selected by analyzing the past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0036] The data collection unit can filter data based on the user's current activity status and areas of interest during data collection. For example, the data collection unit uses sensors and devices to understand the user's current activity status. For instance, it can acquire activity data from the user's smartwatch or fitness tracker to understand the user's current activity status. The data collection unit can also analyze social media and search history to understand the user's areas of interest. For example, it can analyze the user's social media posts and search keywords to identify areas of interest. The data collection unit filters data based on the user's current activity status and areas of interest. For example, if the user is exercising, the data collection unit will prioritize collecting health-related data. The data collection unit can also prioritize collecting business-related data if the user is working. Furthermore, if the user is engrossed in a hobby, the data collection unit can prioritize collecting data related to that hobby. This allows for the collection of highly relevant data by filtering data based on the user's activity status and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user activity data and areas of interest data into the generating AI, and have the generating AI perform data filtering.

[0037] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can use GPS technology to obtain the user's geographical location information. For example, the data collection unit can obtain location information from the user's smartphone or GPS device. The data collection unit can also determine the geographical location using the user's address information. For example, the data collection unit can obtain location information based on the user's registered address or current location. The data collection unit prioritizes the collection of highly relevant data by considering the user's geographical location information. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. The data collection unit can also prioritize the collection of data related to the travel destination if the user is traveling. Furthermore, if the user is at home, the data collection unit can prioritize the collection of data around the user's home. In this way, highly relevant data can be collected by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0038] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can use AI to analyze a user's social media activity. For example, the data collection unit can input the user's social media posts, the number of likes, the number of followers, etc., into the AI ​​and collect relevant data. For example, the data collection unit can collect relevant data based on information shared by the user on social media. The data collection unit can also collect relevant data based on the activity of accounts that the user follows. Furthermore, the data collection unit can collect relevant data based on the activity of groups and communities that the user participates in. For example, the data collection unit can analyze the content of posts in groups that the user participates in and collect relevant data. In this way, relevant data can be collected by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit uses AI to evaluate the importance of the data. For example, the analysis unit inputs the impact and reliability of the data into the AI ​​and evaluates its importance. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can perform an analysis with an appropriate level of detail on data with moderate importance. For example, the analysis unit performs a detailed analysis on high-importance health data to provide information useful for health management. The analysis unit can also perform a simplified analysis on low-importance data to provide only the minimum necessary information. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit uses AI to classify data categories. For instance, the analysis unit inputs categories such as text data, numerical data, and image data into the AI ​​for classification. For example, the analysis unit applies an analysis algorithm specialized for health management to health data. The analysis unit can also apply an analysis algorithm specialized for financial analysis to financial data. Furthermore, the analysis unit can apply an analysis algorithm specialized for lifestyle optimization to lifestyle data. For example, the analysis unit applies an analysis algorithm useful for fitness tracking and diet management to health data. The analysis unit can also apply an analysis algorithm useful for budget management and investment analysis to financial data. This improves the accuracy of the analysis by applying the appropriate analysis algorithm according to the data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data categories into a generating AI and have the generating AI execute the application of an appropriate analysis algorithm.

[0041] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit uses AI to evaluate the data collection period. For example, the analysis unit inputs the collection date and time and collection period into the AI ​​for evaluation. For example, the analysis unit prioritizes the analysis of the most recent data. The analysis unit can also prioritize the most recent data while referring to past data. Furthermore, the analysis unit can prioritize the analysis of data collected during a specific period. For example, the analysis unit prioritizes the analysis of the latest health data to provide information useful for health management. The analysis unit can also prioritize the analysis of the latest financial data while referring to past financial data. This allows for the prioritization of analysis based on the data collection period, thereby prioritizing the analysis of the most recent data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection period into a generating AI and have the generating AI determine the analysis priority.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit may use AI to evaluate the relevance of the data. For example, the analysis unit may input co-occurrence relationships and correlations into the AI ​​for evaluation. For example, the analysis unit may prioritize the analysis of data with high relevance. It may also analyze data with moderate relevance next. Furthermore, it may analyze data with low relevance last. For example, the analysis unit may evaluate the relationship between health data and exercise data and prioritize the analysis of data with high relevance. It may also evaluate the relationship between financial data and expenditure data and analyze data with moderate relevance next. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may input the relevance of the data into a generating AI and have the generating AI adjust the order of analysis.

[0043] The service provider can select the optimal display method by referring to the user's past data usage history at the time of service provision. For example, the service provider may use AI to analyze the user's past data usage history. For example, the service provider may input past usage dates and types of data used into the AI ​​and select the optimal display method. For example, the service provider may prioritize providing display methods that the user has preferred to use in the past. The service provider can also identify the most effective display method from the user's past data usage history. Furthermore, the service provider can eliminate display methods that the user has avoided in the past and provide the optimal display method. For example, the service provider may prioritize providing graphical display methods that the user has preferred to use in the past. The service provider can also identify the most effective text display method from the user's past data usage history. In this way, by referring to past data usage history, the service provider can provide the optimal display method for the user. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input the user's past data usage history into a generating AI and have the generating AI select the optimal display method.

[0044] The information provider can customize information based on the user's current interests at the time of delivery. For example, the provider can use AI to understand the user's current interests. For example, the provider can input the user's social media posts and search history into the AI ​​to identify interests. For example, the provider can prioritize displaying information related to topics the user is currently interested in. The provider can also customize and provide information related to the user's current activities. Furthermore, the provider can provide relevant information based on keywords the user has recently searched for. For example, the provider can prioritize displaying information related to health topics the user is currently interested in. The provider can also customize and provide business information related to the user's current activities. This allows for the provision of more relevant information by customizing information based on the user's interests. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the provider can input user interest data into a generating AI and have the generating AI perform the information customization.

[0045] The information provider can provide optimal information by considering the user's geographical location at the time of delivery. For example, the information provider can use GPS technology to obtain the user's geographical location. For example, the information provider can obtain location information from the user's smartphone or GPS device. The information provider can also determine the geographical location using the user's address information. For example, the information provider can obtain location information based on the user's registered address or current location. The information provider provides optimal information by considering the user's geographical location. For example, if the user is in a specific region, the information provider will prioritize providing information related to that region. The information provider can also prioritize providing information related to the travel destination if the user is traveling. Furthermore, if the user is at home, the information provider can prioritize providing information around the user's home. In this way, by considering geographical location, the information provider can provide information that is highly relevant to the user. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing optimal information.

[0046] The service provider can analyze a user's social media activity and provide relevant information at the time of delivery. For example, the service provider can use AI to analyze a user's social media activity. For example, the service provider can input the user's social media posts, the number of likes, the number of followers, etc., into the AI ​​and provide relevant information. For example, the service provider can provide relevant information based on information shared by the user on social media. The service provider can also provide relevant information based on the activity of accounts that the user follows. Furthermore, the service provider can provide relevant information based on the activity of groups and communities that the user participates in. For example, the service provider can analyze the content of posts in groups that the user participates in and provide relevant information. In this way, by analyzing social media activity, the service provider can provide information that is highly relevant to the user. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI perform the provision of relevant information.

[0047] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0048] The data collection unit monitors the battery level of the user's device and can temporarily stop data collection if the battery is low. For example, the data collection unit periodically checks the battery level of smartphones and wearable devices and stops data collection when the battery level falls below 20%. The data collection unit can also resume data collection when the battery is fully charged. Furthermore, the data collection unit can adjust the frequency of data collection to minimize battery drain. This extends the battery life of the user's device and ensures the continuity of data collection.

[0049] The analytics unit can analyze a user's past behavior patterns and predict their future behavior. For example, it can analyze a user's past travel history to predict the places they are most likely to visit next. It can also analyze a user's past purchase history to predict the products they are most likely to buy next. Furthermore, it can analyze a user's past health data to predict their future health status. By predicting a user's future behavior, it becomes possible to provide more personalized services.

[0050] The data collection unit monitors the network connection status of the user's device and can temporarily suspend data collection if the connection is unstable. For example, the data collection unit periodically checks the Wi-Fi and mobile data connection status and stops data collection if the connection is unstable. The data collection unit can also resume data collection when the connection becomes stable. Furthermore, the data collection unit can adjust the frequency of data collection to reduce the network load. This ensures network connection stability and improves the efficiency of data collection.

[0051] The service provider can adjust the display method according to the screen size of the user's device. For example, it can provide a simple and highly visible display method for the small screen of a smartphone. It can also provide a display method that includes detailed information for the larger screens of tablets and desktops. Furthermore, it can optimize the display method according to the screen resolution of the user's device. This improves the visibility and comprehension of information by providing the optimal display method for the user's device.

[0052] The analysis unit can analyze a user's past data usage history and select the optimal analysis algorithm. For example, it can analyze past analysis results and usage frequency to select the most effective analysis algorithm. Furthermore, the analysis unit can adjust the analysis algorithm based on the user's past feedback. In addition, the analysis unit can analyze the user's past data usage patterns and automatically select the optimal analysis algorithm. This allows for the provision of more accurate analysis results by leveraging the user's past data usage history.

[0053] The data collection unit monitors the user's device storage capacity and can temporarily stop data collection if storage is insufficient. For example, the unit periodically checks the storage capacity of smartphones and tablets and stops data collection if the storage capacity falls below 10%. The unit can also resume data collection when sufficient storage becomes available. Furthermore, the unit can adjust the frequency of data collection to minimize storage consumption. This allows for efficient management of the user's device storage capacity and ensures the continuity of data collection.

[0054] The following briefly describes the processing flow for example form 1.

[0055] Step 1: The collection unit collects data. The collection unit automatically collects data from various platforms and stores it securely. For example, it can collect data using APIs or by using scraping techniques. The collection unit also has data encryption and access control functions, encrypting data using encryption technologies such as AES and RSA, and providing access control functions such as authentication, authorization, and role-based access control. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using generative AI, employing text generation AI (e.g., LLM) and multimodal generation AI. The analysis unit extracts statistical and trend information from the data and applies different analysis algorithms depending on the data category. For example, health data is subjected to an analysis algorithm specifically designed for health management. Step 3: The service provider provides the analysis results obtained by the analysis unit. The service provider visually displays the analysis results in a way that is easy for the individual to understand. For example, the analysis results are displayed in the form of graphs, charts, dashboards, etc. The service provider also estimates the user's emotions and adjusts how the information is displayed based on the estimated emotions of the user. For example, if the user is feeling anxious, a simple and highly visible display method is provided.

[0056] (Example of form 2) The personal data platform according to an embodiment of the present invention is a system that securely aggregates personal data and creates new value by utilizing generative AI. This personal data platform consists of three main elements: a data collection interface, a generative AI, and an analysis result provision interface. The data collection interface automatically collects data from various platforms and stores it securely. The generative AI analyzes the collected data and extracts information useful to the individual. The analysis result provision interface visually displays the analysis results so that individuals can easily understand them. This allows individuals to effectively utilize their own data and improve their quality of life. In terms of security, it is equipped with functions for data encryption and access control. For example, specific applications can be considered such as health management, financial management, and lifestyle optimization. Thus, the personal data platform can effectively utilize personal data and improve the quality of life.

[0057] The personal data platform according to this embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects data. The collection unit automatically collects data from various platforms, for example, and stores it securely. The collection unit collects data using APIs, for example. The collection unit can also collect data using scraping techniques. Furthermore, the collection unit has functions for data encryption and access control. For example, the collection unit encrypts data using encryption technologies such as AES or RSA. The collection unit also has access control functions such as authentication, authorization, and role-based access control. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using generative AI, for example. Generative AI includes text generation AI (e.g., LLM) and multimodal generation AI. The analysis unit extracts statistical information and trend information from the data, for example. The analysis unit can also apply different analysis algorithms depending on the data category. For example, the analysis unit applies a health management-specific analysis algorithm to health data. The provision unit provides the analysis results obtained by the analysis unit. The data provider, for example, visually displays the analysis results so that individuals can easily understand them. The data provider displays the analysis results in the form of graphs, charts, dashboards, etc. The data provider can also estimate the user's emotions and adjust the way the information is displayed based on the estimated emotions. For example, if the data provider is feeling stressed, it provides a simple and highly visible display method. As a result, the personal data platform according to this embodiment can effectively utilize personal data and improve the quality of life.

[0058] The data collection unit can automatically collect data from multiple platforms and store it securely. For example, the data collection unit can automatically collect data from multiple platforms using APIs. For instance, it can collect data from social media, websites, and applications. The data collection unit can also collect data using scraping techniques. For example, it can analyze the content of web pages and extract the necessary data. Furthermore, the data collection unit securely stores the collected data. For example, it can encrypt the data using encryption technologies such as AES or RSA. The data collection unit also includes access control functions such as authentication, authorization, and role-based access control. This enables centralized data management by automatically collecting and securely storing data. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input data acquired using APIs into a generating AI and have the generating AI perform data analysis.

[0059] The analysis unit can analyze the collected data and extract information useful to the individual. For example, the analysis unit can analyze the collected data using generative AI. Generative AI can be text generation AI (e.g., LLM) or multimodal generation AI. The analysis unit can extract statistical information and trend information from the data. For example, the analysis unit can analyze an individual's health status from the collected data and provide information useful for health management. The analysis unit can also analyze an individual's financial situation from the collected data and provide information useful for financial management. Furthermore, the analysis unit can analyze an individual's lifestyle from the collected data and provide information useful for optimizing their lifestyle. For example, the analysis unit can analyze an individual's diet and exercise data and suggest a healthy lifestyle. In this way, by analyzing the data and extracting useful information, individuals can effectively utilize the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generative AI and have the generative AI perform the data analysis.

[0060] The data provider can visually display the analysis results so that individuals can easily understand them. For example, the data provider can visually display the analysis results in the form of graphs, charts, dashboards, etc. For example, the data provider can display a graph showing an individual's health status and provide information useful for health management. The data provider can also display a chart showing an individual's financial situation and provide information useful for financial management. Furthermore, the data provider can display a dashboard showing an individual's lifestyle and provide information useful for optimizing their lifestyle. For example, the data provider can visually display an individual's diet and exercise data and suggest a healthy lifestyle. By visually displaying the analysis results, individuals can easily understand the content of the data. Some or all of the above processing in the data provider may be performed using AI, for example, or not using AI. For example, the data provider can input the analysis results into a generating AI and have the generating AI perform the visual display.

[0061] The data collection unit can be equipped with data encryption and access control functions. The data collection unit encrypts data using encryption technologies such as AES or RSA. For example, the data collection unit encrypts collected data using AES encryption technology and stores it securely. The data collection unit also has access control functions such as authentication, authorization, and role-based access control. For example, the data collection unit authenticates users accessing the data, and only authorized users can access the data. Furthermore, the data collection unit can use role-based access control to set access rights according to the user's role. This enhances data security through data encryption and access control. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have a generating AI perform data encryption and access control settings.

[0062] The analysis unit can provide specific application examples for health management, financial management, and lifestyle optimization. For example, the analysis unit can analyze collected data and provide information useful for health management. For example, the analysis unit can analyze an individual's health status and provide information useful for fitness tracking and diet management. The analysis unit can also analyze collected data and provide information useful for financial management. For example, the analysis unit can analyze an individual's financial situation and provide information useful for budget management and investment analysis. Furthermore, the analysis unit can analyze collected data and provide information useful for lifestyle optimization. For example, the analysis unit can provide information useful for an individual's time management and habit formation. By providing specific application examples, individuals can utilize data in a variety of fields. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input collected data into a generating AI and have the generating AI generate information useful for health management, financial management, and lifestyle optimization.

[0063] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, the data collection unit can use facial recognition technology to estimate the user's emotions. For instance, the data collection unit can capture the user's facial expressions with a camera and estimate their emotions using a facial recognition algorithm. The data collection unit can also estimate the user's emotions using voice analysis technology. For example, the data collection unit can record the user's voice with a microphone and estimate their emotions using a voice analysis algorithm. Furthermore, the data collection unit can also estimate the user's emotions using text analysis technology. For example, the data collection unit can analyze the user's text messages and estimate their emotions. The data collection unit adjusts the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. Conversely, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. Furthermore, if the user is in a hurry, the data collection unit can temporarily stop data collection and resume it later. This reduces the user's burden by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's emotion data into the generative AI and have the generative AI adjust the timing of data collection.

[0064] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit uses AI to analyze the user's past data collection history. For example, the data collection unit inputs past collection dates and types of collected data into the AI ​​and selects the optimal collection method. For example, the data collection unit prioritizes collecting data from platforms that the user has frequently used in the past. The data collection unit can also identify the most efficient collection time slots from the user's past data collection history. Furthermore, the data collection unit can optimize the data to be collected based on the types of data the user has collected in the past. For example, the data collection unit prioritizes collecting health-related data based on health data the user has collected in the past. This allows the optimal collection method to be selected by analyzing the past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0065] The data collection unit can filter data based on the user's current activity status and areas of interest during data collection. For example, the data collection unit uses sensors and devices to understand the user's current activity status. For instance, it can acquire activity data from the user's smartwatch or fitness tracker to understand the user's current activity status. The data collection unit can also analyze social media and search history to understand the user's areas of interest. For example, it can analyze the user's social media posts and search keywords to identify areas of interest. The data collection unit filters data based on the user's current activity status and areas of interest. For example, if the user is exercising, the data collection unit will prioritize collecting health-related data. The data collection unit can also prioritize collecting business-related data if the user is working. Furthermore, if the user is engrossed in a hobby, the data collection unit can prioritize collecting data related to that hobby. This allows for the collection of highly relevant data by filtering data based on the user's activity status and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user activity data and areas of interest data into the generating AI, and have the generating AI perform data filtering.

[0066] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, the data collection unit can use facial recognition technology to estimate the user's emotions. For instance, it can capture the user's facial expressions with a camera and estimate their emotions using a facial recognition algorithm. The data collection unit can also estimate the user's emotions using voice analysis technology. For example, it can record the user's voice with a microphone and estimate their emotions using a voice analysis algorithm. Furthermore, the data collection unit can also estimate the user's emotions using text analysis technology. For example, it can analyze the user's text messages and estimate their emotions. The data collection unit determines the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting data that helps them relax. If the user is excited, the data collection unit can also prioritize collecting data that supports that excitement. Furthermore, if the user is tired, the data collection unit can also prioritize collecting data that helps them rest. By prioritizing data based on the user's emotions, more appropriate data can be collected. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of the data.

[0067] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can use GPS technology to obtain the user's geographical location information. For example, the data collection unit can obtain location information from the user's smartphone or GPS device. The data collection unit can also determine the geographical location using the user's address information. For example, the data collection unit can obtain location information based on the user's registered address or current location. The data collection unit prioritizes the collection of highly relevant data by considering the user's geographical location information. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. The data collection unit can also prioritize the collection of data related to the travel destination if the user is traveling. Furthermore, if the user is at home, the data collection unit can prioritize the collection of data around the user's home. In this way, highly relevant data can be collected by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0068] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can use AI to analyze a user's social media activity. For example, the data collection unit can input the user's social media posts, the number of likes, the number of followers, etc., into the AI ​​and collect relevant data. For example, the data collection unit can collect relevant data based on information shared by the user on social media. The data collection unit can also collect relevant data based on the activity of accounts that the user follows. Furthermore, the data collection unit can collect relevant data based on the activity of groups and communities that the user participates in. For example, the data collection unit can analyze the content of posts in groups that the user participates in and collect relevant data. In this way, relevant data can be collected by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0069] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, the analysis unit can use facial recognition technology to estimate the user's emotions. For instance, it can capture the user's facial expressions with a camera and estimate the emotions using a facial recognition algorithm. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, it can record the user's voice with a microphone and estimate the emotions using a voice analysis algorithm. Furthermore, the analysis unit can also estimate the user's emotions using text analysis technology. For example, it can analyze the user's text messages and estimate the emotions. The analysis unit adjusts the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit uses a visually calm presentation. If the user is excited, the analysis unit can use a visually stimulating presentation. Furthermore, if the user is tired, the analysis unit can use a simple and highly visible presentation. By adjusting the presentation of the analysis based on the user's emotions, the analysis unit can provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the way the analysis is represented.

[0070] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit uses AI to evaluate the importance of the data. For example, the analysis unit inputs the impact and reliability of the data into the AI ​​and evaluates its importance. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can perform an analysis with an appropriate level of detail on data with moderate importance. For example, the analysis unit performs a detailed analysis on high-importance health data to provide information useful for health management. The analysis unit can also perform a simplified analysis on low-importance data to provide only the minimum necessary information. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0071] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit uses AI to classify data categories. For instance, the analysis unit inputs categories such as text data, numerical data, and image data into the AI ​​for classification. For example, the analysis unit applies an analysis algorithm specialized for health management to health data. The analysis unit can also apply an analysis algorithm specialized for financial analysis to financial data. Furthermore, the analysis unit can apply an analysis algorithm specialized for lifestyle optimization to lifestyle data. For example, the analysis unit applies an analysis algorithm useful for fitness tracking and diet management to health data. The analysis unit can also apply an analysis algorithm useful for budget management and investment analysis to financial data. This improves the accuracy of the analysis by applying the appropriate analysis algorithm according to the data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data categories into a generating AI and have the generating AI execute the application of an appropriate analysis algorithm.

[0072] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, the analysis unit can use facial recognition technology to estimate the user's emotions. For instance, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using a facial recognition algorithm. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit can record the user's voice with a microphone and estimate the emotions using a voice analysis algorithm. Furthermore, the analysis unit can also estimate the user's emotions using text analysis technology. For example, the analysis unit can analyze the user's text messages and estimate the emotions. The analysis unit adjusts the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. By adjusting the length of the analysis based on the user's emotions, the analysis unit can provide the user with an appropriate analysis result. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0073] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit uses AI to evaluate the data collection period. For example, the analysis unit inputs the collection date and time and collection period into the AI ​​for evaluation. For example, the analysis unit prioritizes the analysis of the most recent data. The analysis unit can also prioritize the most recent data while referring to past data. Furthermore, the analysis unit can prioritize the analysis of data collected during a specific period. For example, the analysis unit prioritizes the analysis of the latest health data to provide information useful for health management. The analysis unit can also prioritize the analysis of the latest financial data while referring to past financial data. This allows for the prioritization of analysis based on the data collection period, thereby prioritizing the analysis of the most recent data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection period into a generating AI and have the generating AI determine the analysis priority.

[0074] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit may use AI to evaluate the relevance of the data. For example, the analysis unit may input co-occurrence relationships and correlations into the AI ​​for evaluation. For example, the analysis unit may prioritize the analysis of data with high relevance. It may also analyze data with moderate relevance next. Furthermore, it may analyze data with low relevance last. For example, the analysis unit may evaluate the relationship between health data and exercise data and prioritize the analysis of data with high relevance. It may also evaluate the relationship between financial data and expenditure data and analyze data with moderate relevance next. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may input the relevance of the data into a generating AI and have the generating AI adjust the order of analysis.

[0075] The service provider can estimate the user's emotions and adjust the way information is displayed based on those estimated emotions. For example, the service provider can use facial recognition technology to estimate the user's emotions. For instance, it can capture the user's facial expressions with a camera and estimate their emotions using a facial recognition algorithm. The service provider can also estimate the user's emotions using voice analysis technology. For example, it can record the user's voice with a microphone and estimate their emotions using a voice analysis algorithm. Furthermore, the service provider can also estimate the user's emotions using text analysis technology. For example, it can analyze the user's text messages and estimate their emotions. The service provider adjusts the way information is displayed based on the estimated user emotions. For example, if the user is tense, it provides a simple and highly visible display method. If the user is relaxed, it can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display method that gets straight to the point. By adjusting the way information is displayed based on the user's emotions, the service provider can provide information that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, by using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, or not using AI. For example, the service provider can input user emotion data into the generative AI and have the generative AI adjust how the information is displayed.

[0076] The service provider can select the optimal display method by referring to the user's past data usage history at the time of service provision. For example, the service provider may use AI to analyze the user's past data usage history. For example, the service provider may input past usage dates and types of data used into the AI ​​and select the optimal display method. For example, the service provider may prioritize providing display methods that the user has preferred to use in the past. The service provider can also identify the most effective display method from the user's past data usage history. Furthermore, the service provider can eliminate display methods that the user has avoided in the past and provide the optimal display method. For example, the service provider may prioritize providing graphical display methods that the user has preferred to use in the past. The service provider can also identify the most effective text display method from the user's past data usage history. In this way, by referring to past data usage history, the service provider can provide the optimal display method for the user. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input the user's past data usage history into a generating AI and have the generating AI select the optimal display method.

[0077] The information provider can customize information based on the user's current interests at the time of delivery. For example, the provider can use AI to understand the user's current interests. For example, the provider can input the user's social media posts and search history into the AI ​​to identify interests. For example, the provider can prioritize displaying information related to topics the user is currently interested in. The provider can also customize and provide information related to the user's current activities. Furthermore, the provider can provide relevant information based on keywords the user has recently searched for. For example, the provider can prioritize displaying information related to health topics the user is currently interested in. The provider can also customize and provide business information related to the user's current activities. This allows for the provision of more relevant information by customizing information based on the user's interests. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the provider can input user interest data into a generating AI and have the generating AI perform the information customization.

[0078] The service provider can estimate the user's emotions and prioritize the information to be provided based on those estimated emotions. For example, the service provider can use facial recognition technology to estimate the user's emotions. For instance, it can capture the user's facial expressions with a camera and estimate their emotions using a facial recognition algorithm. The service provider can also estimate the user's emotions using voice analysis technology. For example, it can record the user's voice with a microphone and estimate their emotions using a voice analysis algorithm. Furthermore, the service provider can also estimate the user's emotions using text analysis technology. For example, it can analyze the user's text messages and estimate their emotions. The service provider then prioritizes the information to be provided based on the estimated user emotions. For example, if the user is feeling stressed, the service provider will prioritize providing information that helps them relax. If the user is excited, the service provider will prioritize providing information that supports that excitement. Furthermore, if the user is tired, the service provider will prioritize providing information that helps them rest. By prioritizing information based on the user's emotions, the service provider can provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, by using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into the generative AI and have the generative AI determine the priority of the information.

[0079] The information provider can provide optimal information by considering the user's geographical location at the time of delivery. For example, the information provider can use GPS technology to obtain the user's geographical location. For example, the information provider can obtain location information from the user's smartphone or GPS device. The information provider can also determine the geographical location using the user's address information. For example, the information provider can obtain location information based on the user's registered address or current location. The information provider provides optimal information by considering the user's geographical location. For example, if the user is in a specific region, the information provider will prioritize providing information related to that region. The information provider can also prioritize providing information related to the travel destination if the user is traveling. Furthermore, if the user is at home, the information provider can prioritize providing information around the user's home. In this way, by considering geographical location, the information provider can provide information that is highly relevant to the user. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing optimal information.

[0080] The service provider can analyze a user's social media activity and provide relevant information at the time of delivery. For example, the service provider can use AI to analyze a user's social media activity. For example, the service provider can input the user's social media posts, the number of likes, the number of followers, etc., into the AI ​​and provide relevant information. For example, the service provider can provide relevant information based on information shared by the user on social media. The service provider can also provide relevant information based on the activity of accounts that the user follows. Furthermore, the service provider can provide relevant information based on the activity of groups and communities that the user participates in. For example, the service provider can analyze the content of posts in groups that the user participates in and provide relevant information. In this way, by analyzing social media activity, the service provider can provide information that is highly relevant to the user. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI perform the provision of relevant information. === Hard Collateral 1-1 === Each of the multiple elements described above, including the data collection unit, analysis unit, and data provision unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects data via the communication I / F 44 of the smart device 14 and securely stores it by the identification processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using a generation AI. The data provision unit is implemented, for example, by the control unit 46A of the smart device 14 and visually displays the analysis results. === Hard Collateral 1-2 === Each of the multiple elements described above, including the data collection unit, analysis unit, and data provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects data via the communication I / F 44 of the smart glasses 214 and securely stores it by the identification processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using generating AI. The data provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and visually displays the analysis results. === Hard Collateral 1-3 === Each of the multiple elements described above, including the collection unit, analysis unit, and provision unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects data via the communication I / F 44 of the headset terminal 314 and securely stores it by the identification processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using a generation AI. The provision unit is implemented, for example, by the control unit 46A of the headset terminal 314 and visually displays the analysis results. === Hard Collateral 1-4 === Each of the multiple elements described above, including the data collection unit, analysis unit, and data provision unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects data via the communication I / F 44 of the robot 414 and securely stores it by the identification processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using a generated AI. The data provision unit is implemented, for example, by the control unit 46A of the robot 414 and visually displays the analysis results.

[0081] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0082] The data collection unit monitors the battery level of the user's device and can temporarily stop data collection if the battery is low. For example, the data collection unit periodically checks the battery level of smartphones and wearable devices and stops data collection when the battery level falls below 20%. The data collection unit can also resume data collection when the battery is fully charged. Furthermore, the data collection unit can adjust the frequency of data collection to minimize battery drain. This extends the battery life of the user's device and ensures the continuity of data collection.

[0083] The analytics unit can analyze a user's past behavior patterns and predict their future behavior. For example, it can analyze a user's past travel history to predict the places they are most likely to visit next. It can also analyze a user's past purchase history to predict the products they are most likely to buy next. Furthermore, it can analyze a user's past health data to predict their future health status. By predicting a user's future behavior, it becomes possible to provide more personalized services.

[0084] The system can estimate the user's emotions and adjust the timing of notifications based on those emotions. For example, if the user is relaxed, the system can send a notification immediately. It can also delay notifications if the user is stressed. Furthermore, if the user is concentrating, the system can temporarily suppress notifications and send them all at once later. This allows for reduced user stress and maximized notification effectiveness by adjusting notification timing according to the user's emotions.

[0085] The data collection unit monitors the network connection status of the user's device and can temporarily suspend data collection if the connection is unstable. For example, the data collection unit periodically checks the Wi-Fi and mobile data connection status and stops data collection if the connection is unstable. The data collection unit can also resume data collection when the connection becomes stable. Furthermore, the data collection unit can adjust the frequency of data collection to reduce the network load. This ensures network connection stability and improves the efficiency of data collection.

[0086] The analysis unit can estimate the user's emotions and adjust the level of detail in the analysis results based on the estimated emotions. For example, if the user is relaxed, the analysis unit will provide detailed results. If the user is stressed, the analysis unit can provide concise results. Furthermore, if the user is excited, the analysis unit can provide visually stimulating results. By adjusting the level of detail in the analysis results according to the user's emotions, the system can provide the user with the most relevant information.

[0087] The service provider can adjust the display method according to the screen size of the user's device. For example, it can provide a simple and highly visible display method for the small screen of a smartphone. It can also provide a display method that includes detailed information for the larger screens of tablets and desktops. Furthermore, it can optimize the display method according to the screen resolution of the user's device. This improves the visibility and comprehension of information by providing the optimal display method for the user's device.

[0088] The data collection unit can estimate the user's emotions and adjust the frequency of data collection based on those estimates. For example, the unit increases the frequency of data collection when the user is relaxed. It can also decrease the frequency of data collection when the user is stressed. Furthermore, it can temporarily increase the frequency of data collection when the user is excited. By adjusting the frequency of data collection according to the user's emotions, the unit can reduce the user's burden and improve the quality of the data.

[0089] The analysis unit can analyze a user's past data usage history and select the optimal analysis algorithm. For example, it can analyze past analysis results and usage frequency to select the most effective analysis algorithm. Furthermore, the analysis unit can adjust the analysis algorithm based on the user's past feedback. In addition, the analysis unit can analyze the user's past data usage patterns and automatically select the optimal analysis algorithm. This allows for the provision of more accurate analysis results by leveraging the user's past data usage history.

[0090] The information provider can estimate the user's emotions and adjust the amount of information provided based on those emotions. For example, if the user is relaxed, the provider can provide detailed information. If the user is stressed, the provider can provide concise information. Furthermore, if the user is excited, the provider can provide visually stimulating information. By adjusting the amount of information provided according to the user's emotions, the provider can deliver information that is optimal for the user.

[0091] The data collection unit monitors the user's device storage capacity and can temporarily stop data collection if storage is insufficient. For example, the unit periodically checks the storage capacity of smartphones and tablets and stops data collection if the storage capacity falls below 10%. The unit can also resume data collection when sufficient storage becomes available. Furthermore, the unit can adjust the frequency of data collection to minimize storage consumption. This allows for efficient management of the user's device storage capacity and ensures the continuity of data collection.

[0092] The following briefly describes the processing flow for example form 2.

[0093] Step 1: The collection unit collects data. The collection unit automatically collects data from various platforms and stores it securely. For example, it can collect data using APIs or by using scraping techniques. The collection unit also has data encryption and access control functions, encrypting data using encryption technologies such as AES and RSA, and providing access control functions such as authentication, authorization, and role-based access control. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using generative AI, employing text generation AI (e.g., LLM) and multimodal generation AI. The analysis unit extracts statistical and trend information from the data and applies different analysis algorithms depending on the data category. For example, health data is subjected to an analysis algorithm specifically designed for health management. Step 3: The service provider provides the analysis results obtained by the analysis unit. The service provider visually displays the analysis results in a way that is easy for the individual to understand. For example, the analysis results are displayed in the form of graphs, charts, dashboards, etc. The service provider also estimates the user's emotions and adjusts how the information is displayed based on the estimated emotions of the user. For example, if the user is feeling anxious, a simple and highly visible display method is provided.

[0094] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0095] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0096] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0097] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0098] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0099] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0100] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0101] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0102] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0103] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0104] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0105] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0106] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0107] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0108] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0109] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0111] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0112] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0113] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0114] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0115] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0117] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0118] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0121] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0122] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0130] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0131] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0138] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0147] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0148] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0149] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0150] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0151] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0152] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0153] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0154] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0155] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0156] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0157] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0158] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0159] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0160] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0161] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0162] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0163] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0164] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0165] [Explanation of symbols]

[0166] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A providing unit that provides the analysis results obtained by the aforementioned analysis unit, Equipped with A system characterized by the following features.

2. The aforementioned collection unit is Automatically collect data from multiple platforms and store it securely. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed, and information useful to the individual is extracted. The system according to feature 1.

4. The aforementioned supply unit is, The analysis results are displayed visually, making them easy for individuals to understand. The system according to feature 1.

5. The aforementioned collection unit is It includes features for data encryption and access control. The system according to feature 1.

6. The aforementioned analysis unit, This provides concrete application examples for health management, financial management, and lifestyle optimization. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past data collection history and select the optimal collection method. The system according to feature 1.

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